Description:Infection Control and Hospital Epidemiology provides original, peer-reviewed scientific articles for anyone involved with an infection control or epidemiology program in a hospital or healthcare facility. Written by infection control practitioners and epidemiologists and guided by an editorial board composed of the nation's leaders in the field, ICHE provides a critical forum for this vital information.

The "moving wall" represents the time period between the last issue
available in JSTOR and the most recently published issue of a journal.
Moving walls are generally represented in years. In rare instances, a
publisher has elected to have a "zero" moving wall, so their current
issues are available in JSTOR shortly after publication.
Note: In calculating the moving wall, the current year is not counted.
For example, if the current year is 2008 and a journal has a 5 year
moving wall, articles from the year 2002 are available.

Terms Related to the Moving Wall

Fixed walls: Journals with no new volumes being added to the archive.

Absorbed: Journals that are combined with another title.

Complete: Journals that are no longer published or that have been
combined with another title.

Abstract

OBJECTIVES. To identify institution‐specific risk factors for MRSA bacteremia and develop an objective mechanism to estimate the probability of methicillin resistance in a given patient with Staphylococcus aureus bacteremia (SAB). DESIGN . A cohort study was performed to identify institution‐specific risk factors for MRSA. Logistic regression was used to model the likelihood of MRSA. A stepwise approach was employed to derive a parsimonious model. The MRSA prediction tool was developed from the final model. SETTING. A 279‐bed, level 1 trauma center. PATIENTS. Between January 1, 1999, and June 30, 2001, 494 patients with clinically significant episodes of SAB were identified. RESULTS. The MRSA rate was 45.5%. Of 18 characteristics included in the logistic regression, the only independent features for MRSA were prior antibiotic exposure (OR, 9.2; CI95, 4.8 to 17.9), hospital onset (OR, 3.0; CI95, 1.9 to 4.9), history of hospitalization (OR, 2.5; CI95, 1.5 to 3.8), and presence of decubitus ulcers (OR, 2.5; CI95, 1.2 to 4.9). The prediction tool was derived from the final model, which was shown to accurately reflect the actual MRSA distribution in the cohort. CONCLUSION. Through multivariate modeling techniques, we were able to identify the most important determinants of MRSA at our institution and develop a tool to predict the probability of methicillin resistance in a patient with SAB. This knowledge can be used to guide empiric antibiotic selection. In the era of antibiotic resistance, such tools are essential to prevent indiscriminate antibiotic use and preserve the longevity of current antimicrobials.